***************** Replication of Franchino, Fabio, and Camilla Mariotto. “Bargaining Outcomes and Success in EU Economic Governance Reforms”. Political Science Research and Methods.

* Step 2 to reproduce Figure A6

version 16

use betas_noise.dta, clear 

eclplot distSQ distSQ_u distSQ_l v, estopts(msize(small)) rplottype(rspike) ciopts(msize(small)) graphregion(fcolor(white)) xlabel(, labsize(2)) ylabel(, nogrid labsize(2)) title("Distance to reference point", size(2.5)) saving(distSQ_noise, replace) xlabel(1 "1" 20 "20" 40 "40", labsize(2)) ylabel(, labsize(2)) xtitle("St.dev. of Gaussian noise", size(2)) ytitle("Mean absolute error", size(2))  text(40 1.1 "Min 31.5" "Max 37.5", size(2) place(se) just(left) box fc(white) margin(l+1 t+1 b+1) width(10)) yline(0, lc(black) lw(vthin))

eclplot distCOM distCOM_u distCOM_l v, estopts(msize(small)) rplottype(rspike) ciopts(msize(small)) graphregion(fcolor(white)) xlabel(, labsize(2)) ylabel(, nogrid labsize(2)) title("Distance to Commission position", size(2.5)) saving(distCOM_noise, replace) xlabel(1 "1" 20 "20" 40 "40", labsize(2)) ylabel(, labsize(2)) xtitle("St.dev. of Gaussian noise", size(2)) ytitle("Mean absolute error", size(2))  text(40 1.1 "Min 31.5" "Max 37.5", size(2) place(se) just(left) box fc(white) margin(l+1 t+1 b+1) width(10)) yline(0, lc(black) lw(vthin))

eclplot distPRES distPRES_u distPRES_l v, estopts(msize(small)) rplottype(rspike) ciopts(msize(small)) graphregion(fcolor(white)) xlabel(, labsize(2)) ylabel(, nogrid labsize(2)) title("Distance to Council President position", size(2.5)) saving(distPRES_noise, replace) xlabel(1 "1" 20 "20" 40 "40", labsize(2)) ylabel(, labsize(2)) xtitle("St.dev. of Gaussian noise", size(2)) ytitle("Mean absolute error", size(2))  text(40 1.1 "Min 31.5" "Max 37.5", size(2) place(se) just(left) box fc(white) margin(l+1 t+1 b+1) width(10)) yline(0, lc(black) lw(vthin))

eclplot distEP distEP_u distEP_l v, estopts(msize(small)) rplottype(rspike) ciopts(msize(small)) graphregion(fcolor(white)) xlabel(, labsize(2)) ylabel(, nogrid labsize(2)) title("Distance to Parliament position, excluding OLP", size(2.5)) saving(distEP_noise, replace) xlabel(1 "1" 20 "20" 40 "40", labsize(2)) ylabel(, labsize(2)) xtitle("St.dev. of Gaussian noise", size(2)) ytitle("Mean absolute error", size(2))  text(40 1.1 "Min 31.5" "Max 37.5", size(2) place(se) just(left) box fc(white) margin(l+1 t+1 b+1) width(10)) yline(0, lc(black) lw(vthin))

eclplot ep_rule_distEP_2 ep_rule_distEP_2_u ep_rule_distEP_2_l v, estopts(msize(small)) rplottype(rspike) ciopts(msize(small)) graphregion(fcolor(white)) xlabel(, labsize(2)) ylabel(, nogrid labsize(2)) title("Distance to Parliament position, under OLP", size(2.5)) saving(ep_rule_distEP_2_noise, replace) xlabel(1 "1" 20 "20" 40 "40", labsize(2)) ylabel(, labsize(2)) xtitle("St.dev. of Gaussian noise", size(2)) ytitle("Mean absolute error", size(2))  text(40 1.1 "Min 31.5" "Max 37.5", size(2) place(se) just(left) box fc(white) margin(l+1 t+1 b+1) width(10)) yline(0, lc(black) lw(vthin))

eclplot extreme extreme_u extreme_l v, estopts(msize(small)) rplottype(rspike) ciopts(msize(small)) graphregion(fcolor(white)) xlabel(, labsize(2)) ylabel(, nogrid labsize(2)) title("Extremeness of positions", size(2.5)) saving(extreme_noise, replace) xlabel(1 "1" 20 "20" 40 "40", labsize(2)) ylabel(, labsize(2)) xtitle("St.dev. of Gaussian noise", size(2)) ytitle("Mean absolute error", size(2))  text(40 1.1 "Min 31.5" "Max 37.5", size(2) place(se) just(left) box fc(white) margin(l+1 t+1 b+1) width(10))

eclplot v_power v_power_u v_power_l v, estopts(msize(small)) rplottype(rspike) ciopts(msize(small)) graphregion(fcolor(white)) xlabel(, labsize(2)) ylabel(, nogrid labsize(2)) title("Voting power", size(2.5)) saving(v_power_noise, replace) xlabel(1 "1" 20 "20" 40 "40", labsize(2)) ylabel(, labsize(2)) xtitle("St.dev. of Gaussian noise", size(2)) ytitle("Mean absolute error", size(2))  text(40 1.1 "Min 31.5" "Max 37.5", size(2) place(se) just(left) box fc(white) margin(l+1 t+1 b+1) width(10)) yline(0, lc(black) lw(vthin))

eclplot salience_r salience_r_u salience_r_l v, estopts(msize(small)) rplottype(rspike) ciopts(msize(small)) graphregion(fcolor(white)) xlabel(, labsize(2)) ylabel(, nogrid labsize(2)) title("Issue salience", size(2.5)) saving(salience_r_noise, replace) xlabel(1 "1" 20 "20" 40 "40", labsize(2)) ylabel(, labsize(2)) xtitle("St.dev. of Gaussian noise", size(2)) ytitle("Mean absolute error", size(2))  text(40 1.1 "Min 31.5" "Max 37.5", size(2) place(se) just(left) box fc(white) margin(l+1 t+1 b+1) width(10)) yline(0, lc(black) lw(vthin))


gr combine distSQ_noise.gph distCOM_noise.gph distPRES_noise.gph distEP_noise.gph ep_rule_distEP_2_noise.gph extreme_noise.gph v_power_noise.gph salience_r_noise.gph, cols(2) graphregion(fcolor(white)) saving(figure_A6, replace) 

